Meter every workload
Attribute tokens, calls, retries and provider spend to the team, feature and task creating them.
OutputA cost baseline your finance and engineering teams can reconcile.AI cost optimization
Know what every AI workload actually costs.
We attribute inference spend to the features creating it, test cheaper paths against your own quality bar, and report the result on the same production traffic. No borrowed percentage and no savings promise before the meter exists.
The cost loop
Meter. Change. Prove.
Cost optimization is an engineering loop, not a model-price spreadsheet. Each change has to preserve the task quality the business depends on.
Change the expensive path
Test model tiering, routing, caching, prompt reduction and batching where the traffic supports them.
OutputOne production change released behind an evaluation threshold.Prove what moved
Compare cost, latency and task quality on the same traffic rather than projecting savings from a benchmark.
OutputA reproducible before-and-after and a ranked optimization backlog.What you receive
A cost result your team can reproduce.
The audit creates the instrumentation and evidence needed to decide whether further optimization work is worth funding.
- 01Per-workload spend map
Provider, model, feature, team, token volume, retry rate and effective cost per completed task.
- 02Evaluation set and quality floor
Real customer cases that define the quality a cheaper route must hold before it can ship.
- 03Optimization register
Each opportunity ranked by measured cost, expected effort, risk and the evidence required to release it.
- 04Before-and-after report
Observed production cost and quality after the change, with assumptions and exclusions written down.
Straight answers
The questions buyers actually ask.
AI cost optimization attributes provider and infrastructure spend to individual workloads, then tests changes such as model routing, caching, prompt reduction and batching against a defined quality threshold.
Sometimes. Zylen builds an evaluation set from your own cases and records the current quality level first. A cheaper route ships only when it meets the agreed threshold; requests that do not pass stay on the existing path.
We need provider billing and usage data, application telemetry or repository access for the selected workload, and a product or engineering owner who can define acceptable output quality. Missing instrumentation can be added during the audit.
Ten days. One workload changed. Same quality bar.
Put a number on your AI spend.
Bring the provider bill and access to the teams shipping AI. We will map the spend, select one defensible change and measure it in production.